Troubleshooting Python Machine Learning [Video]

Troubleshooting Python Machine Learning [Video]

Rudy Lai

Practical and unique solutions to common Machine Learning problems that you face. Avoid any roadblocks while working with the Python data science ecosystem.
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Video Details

ISBN 13 9781788999229
Course Length 3 hours and 17 minutes

Video Description

You are a data scientist. Every day, you stare at reams of data trying to apply the latest and brightest of models to uncover new insights, but there seems to be an endless supply of obstacles. Your colleagues depend on you to monetize your firm's data - and the clock is ticking. What do you do?

Troubleshooting Python Machine Learning is the answer. We have systematically researched common ML problems documented online around data wrangling, debugging models such as Random Forests and SVMs, and visualizing tricky results. We leverage statistics from Stack Overflow, Medium, and GitHub to get a cross-section of what data scientists struggle with. We have collated for you the top issues, such as retrieving the most important regression features and explaining your results after clustering, and their corresponding solutions. We present these case studies in a problem-solution format, making it very easy for you to incorporate this into your knowledge.

Taking this course will help you to precisely debug your models and research pipelines, so you can focus on pitching new ideas and not fixing old bugs.

All the code and supporting files are available on GitHub at https://github.com/PacktPublishing/Troubleshooting-Python-Machine-Learning

Style and Approach

The course is full of hands-on instructions, interesting and illustrative visualizations, and, clear explanations from a data scientist. It is packed full of useful tips and relevant advice. Throughout the course, we maintain a focus on practicality and getting things done, not fancy mathematical theory.

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What You Will Learn

  • Eliminate common data wrangling problems in Pandas and scikit-learn 
  • Defeat regression and classification difficulties in scikit-learn 
  • Troubleshoot advanced models such as Random Forests and SVMs 
  • Wrangling with unsupervised learning and the curse of dimensionality 
  • Solving prediction visualization issues with matplotlib 
  • Explaining your results with the most important insights
  • Visualizing your decision trees
  • Perform common natural language processing featuring engineering tasks

Authors

Video Details

ISBN 139781788999229
Course Length3 hours and 17 minutes
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